MOUNTAIN THEORY VS AIRIA
New here? The short version: Mountain Theory checks each action an AI is about to take, against rules you write in plain English, before the action runs. Everything below compares that with what Airia does.
Airia is the best funded company in this comparison set and the closest to Mountain Theory on language. Both talk about a control layer, runtime policy and audit-ready evidence. The difference is that Airia is also the platform you build the agents on, and Mountain Theory only governs them.
| Airia | Mountain Theory | |
|---|---|---|
| What it controls | Agent build, deployment, permissions, content filtering | The action an AI agent is about to take |
| Where it sits | In the Airia platform the agents run on | Inline at execution, between the decision and the action |
| How policy is set | Platform roles, guardrails and firewall rules | Plain English, no code |
| Deployment reach | Agents built and run on Airia, plus connected external agents | Model and framework agnostic, including custom and on-prem agents |
| Best fit when | You want one vendor to build, run and secure enterprise AI | An AI acting wrongly has physical or regulatory consequences |
Why you might pick Airia
Airia was founded in 2024 and announced $100MM in September 2025, committed by co-founder and chairman John Marshall, who co-founded AirWatch. Half was deployed at announcement. CEO is Kevin Kiley. Within roughly 12 months of leaving stealth they reported 300+ enterprise customers and 150 employees across five continents, with offices in Atlanta, London, Singapore, Dubai, Melbourne, Sophia and Bangalore. The architecture is model agnostic and they describe a portfolio of granted patents with more pending. If you want one vendor to build, run and secure enterprise AI, that is a serious offer and they have the resources behind it.
Why you might pick Mountain Theory
Two differences, both structural rather than a judgement about how well their product works. First, Airia describes its controls as enterprise-grade guardrails, role-based permissions and AI firewalls. Those govern who may use what and what text passes. Mountain Theory evaluates the action itself at the point of execution and returns ALLOW, HOLD or BLOCK before it runs. Second, Airia is the orchestration platform as well as the security layer, so the system that builds the agent is the system that vouches for it. Mountain Theory governs agents it did not build, wherever they run, including agents built on someone else's platform.
The honest verdict
Ask both vendors the same evidence question and compare the answers, which is the test we publish and invite on ourselves: the exact action set attempted, the ungoverned control condition, the outcome for every action including the ones the product did not stop, and the recording. Then ask who owns the control. If the platform that builds your agents is also the platform that certifies them, an auditor has one party attesting to its own work. That is a separation-of-duties question, and for a regulated buyer it usually decides itself.
What we can actually show
Claims in this category are easy to make and hard to check, so here is ours on the record. The same 10 actions were run in the same order under three configurations. Ungoverned, 10 of 10 executed. Under NVIDIA OpenShell alone, all 5 sandbox-boundary crossings were denied at the kernel, and all 3 in-bounds bad decisions still went through, including a secrets read that printed credentials to the screen. Under OpenShell plus Mountain Theory, those same 3 actions returned HOLD, HOLD and BLOCK, and the secrets read was stopped before it executed, so the credentials never printed. Terminal recordings of all three runs are published, including the two actions Mountain Theory has no policy for.
Separately, when a third-party provider updated the foundation model driving an autonomous agent, the agent began attempting multi-step actions it had never tried before. Nothing on our side changed. Every attempt was stopped on 30 and 31 July 2026, the days the behaviour first appeared. No new rule, no signature, no patch.
Both products in this comparison operate at the execution layer. What execution-layer security is, and how it differs from prompt filtering and from access control.
Watch the three-configuration run against NVIDIA OpenShell
See novel agent behaviour stopped the day it appeared
Ask Airia, and every other vendor you are evaluating, for the same four things: the exact action set, the ungoverned control condition, the outcome per action including the ones the product did not stop, and the recording. A certification, an integration list or a customer logo answers a different question.
Compare all 61 AI security vendors